{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import print_function\n",
    "from statsmodels.compat import lzip\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beef = pd.read_csv('beef.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>22.9976</td>\n",
       "      <td>142.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>22.6131</td>\n",
       "      <td>143.9333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>23.4054</td>\n",
       "      <td>146.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977</td>\n",
       "      <td>4</td>\n",
       "      <td>22.7401</td>\n",
       "      <td>150.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1978</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0441</td>\n",
       "      <td>160.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1978</td>\n",
       "      <td>2</td>\n",
       "      <td>21.7602</td>\n",
       "      <td>182.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1978</td>\n",
       "      <td>3</td>\n",
       "      <td>21.6064</td>\n",
       "      <td>186.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978</td>\n",
       "      <td>4</td>\n",
       "      <td>21.8814</td>\n",
       "      <td>186.4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1979</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5086</td>\n",
       "      <td>211.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0408</td>\n",
       "      <td>231.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Quarter  Quantity     Price\n",
       "0  1977        1   22.9976  142.1667\n",
       "1  1977        2   22.6131  143.9333\n",
       "2  1977        3   23.4054  146.5000\n",
       "3  1977        4   22.7401  150.8000\n",
       "4  1978        1   22.0441  160.0000\n",
       "5  1978        2   21.7602  182.5333\n",
       "6  1978        3   21.6064  186.2000\n",
       "7  1978        4   21.8814  186.4333\n",
       "8  1979        1   20.5086  211.7000\n",
       "9  1979        2   19.0408  231.5000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beef.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year        0\n",
       "Quarter     0\n",
       "Quantity    0\n",
       "Price       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beef.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis\n",
    "\n",
    "### Ordinary Least Squares (OLS) Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "beef_model = ols(\"Quantity ~ Price\", data=beef).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Quantity   R-squared:                       0.901\n",
      "Model:                            OLS   Adj. R-squared:                  0.900\n",
      "Method:                 Least Squares   F-statistic:                     811.2\n",
      "Date:                Fri, 31 Aug 2018   Prob (F-statistic):           1.69e-46\n",
      "Time:                        23:00:04   Log-Likelihood:                -77.493\n",
      "No. Observations:                  91   AIC:                             159.0\n",
      "Df Residuals:                      89   BIC:                             164.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     30.0515      0.413     72.701      0.000      29.230      30.873\n",
      "Price         -0.0465      0.002    -28.482      0.000      -0.050      -0.043\n",
      "==============================================================================\n",
      "Omnibus:                        3.453   Durbin-Watson:                   1.533\n",
      "Prob(Omnibus):                  0.178   Jarque-Bera (JB):                2.460\n",
      "Skew:                           0.237   Prob(JB):                        0.292\n",
      "Kurtosis:                       2.349   Cond. No.                     1.74e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.74e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(beef_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The small P values indicate that we can reject the null hypothesis that Price has no effect on Quantity.\n",
    "\n",
    "Hight r squared indicates that our model explains a lot of the response variability.\n",
    "\n",
    "In regression analysis, we'd like our regression model to have significant variables and to produce a high R-squared value.\n",
    "\n",
    "We will show graphs to help interpret regression analysis results more intuitively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f75d65b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_partregress_grid(beef_model, fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trend indicates that the predictor variables (Price) provides information about the response (Quantity), and data points do not fall further from the regression line, and the predictions are very precise given a prediction interval that extends from about 29 to 31."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Component-Component plus Residual (CCPR) Plots\n",
    "\n",
    "The CCPR plot provides a way to judge the effect of one regressor on the response variable by taking into account the effects of the other independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bNzUM2YO1eVPDCM9Kv/qqIj5242I+duNiTp7r5rGdMSKtMf7zlTf51raD5OdksX55NeFQkA2hAIFSjYAXERERERlKxpYIjlUmlAhCeqcIptqFnj6e33d8YAR8e0cnZrBqQcVA39byQIlGwIuIiIjIjDfte7DGKlMSrJnK3Wk7fJpIS7yU8OU3TgJQX1mU6NsKcM2iSnKzNQJeRERERGYeJVgypFStsEVPdbGlNUakNcpTu49yoaePsoIcbl0ZIBwKcnNDDWUFuZPwCkREREREpp4SLLnEZO3Tde5CD0/uio+A39oW4/jZC+RmG+uWVBEOBdkYCjB/TlEqXoKIiIiISFoowZJLTMU+Xb19zvYDJ2hujRJpibLnyFkAQvPKaArFpxJeXluuEfAiIiIiMq3MhDHtkmKHhkiuRjo+HtlZxtpFlaxdVMk9Px1i75EzbGmN0dwa5a8e3c1fbt1NsCyfjaEgTaEg1y+toiBXI+BFREREZGZQgjXF0jllMB37dC2pKWFJTQm/etMSTpy9wKM74n1b39vezr89d4CivOy3RsCvDFBVkj9psYiIiIiITDYlWFPo4h6o9o5O7nngFYApSbLSvU/XnOI87rh6PndcPZ/zPb08s+dYfIPjlhiPvBbFDNbUz6GpMUi4McjSmpIpiUtEREREJFXUgzWFpqIHajSZuE+Xu/PaoVM0J0bAv3boFABLqosJNwYJh4JcXV9BjkbAi4iIiEiaaMhFBlp898MM9W4bsO++d011OBmrvaOTra1RmltjPLPnKN29zpyiXG5dGaApFGT9ihpK8rX4KiIiIiJTR0MuMlA6eqCmo7qKQj56/SI+ev0iTnd18+Suo0QSI+AfeLGdvOws3rHsrRHw88r1/omIiIhIZtAK1hSarH2oZoue3j627T9BpCVKc2uU/cfOAXBFXTnhUJBwY4DGeWWYaQS8iIiIiKSWSgQzVCb2QE1H7s6eI2dobolPJXzxwAncoba8YKBva92SKvJy1LclIiIiIhOnBEtmlaNnzrO1LUakJcqTu47S2d1LSX4ON6+ooakxyC0NNVQU5aU7TBERERGZppRgyazV1d3L07uPxkfAt8Y4cvo82VnGNYvmEA4FaWoMsrCqON1hioiIiMg0ogRLBOjrc37SfpJIYgR82+HTACwPlAyUEq5aUEF2lvq2RERERGR4SrBEhnDw+LnEylaU5/Yep6fPqS7JY8PKAOFQkPXLayjMy053mCIiIiKSYZRgiYziZGc3j+88QqQlyqM7Ypzu6iE/J4v1y6sJh4JsCAUIlBakO0wRERERyQDaB0tkFOWFubznqlrec1UtF3r6+PHrx2lOlBJGWmMArFpQQVOilHBFsEQj4EVERERkRFrBErmIu7Mjejqx31aMlw92ALCgsjA+JCMU5JrFleRmawS8iIiIyGyhEkGRFImd6mJLYgT8U7uPcr6nj9KCHG5tCBBOjIAvK8hNd5giIiIiMomUYIlMgnMXenhq11GaW6JsbYtx7OwFcrKMdUuqCIcCbAwFWVBZlO4wRURERCTFpnWCZWargL8DCoAe4Nfc/fmRnqMES6Zab5/z0sETNLfEiLRG2R07A8DKuaUDfVtX1JWTpRHwIiIiItPedE+wfgj8mbv/l5n9DPB77n7LSM9RgjWzPLi9nfsf2cGhjk5qKwrZvKmB21fXpTusEe07epYtrVGaW6L8+PXj9DkESvPZGArS1BjgHUurKcjVCHgRERGR6Wi6TxF0oCzxczlwKI2xyBR7cHs79zzwCp3dvQC0d3RyzwOvAGR0krW4upiPr1/Cx9cv4cTZCzy2M0akJcb3X2rnG88foDA3m5tWJEbArwxQVZKf7pBFREREJMUydQUrBDwCGJAFvMPd94/0HK1gzRw33LeV9o7OS47XVRTy9N0b0hDRxJzv6eXZvceJJEbAv3myCzNYUz+HcKKUcGlNsUbAi4iIiGSwjC8RNLMIMHeIuz4NbAQed/f/MLMPAne5e3iIc9wF3AVQX1+/Zv/+EXMwmSYW3/0wQ/2tNGDffe+a6nBSyt157dCpxF5bUV5tPwXEV7/CoQDhUJA1C+eQoxHwIiIiIhkl4xOskZjZSaDC3d3iv9Y/6e5lIz1HK1gzx0xbwRrJoY7OgRHwz+w5xoXePiqKctmQGAF/04oaSvIztZJXREREZPaY7j1Yh4CbgceADcCutEYjU2rzpoa39WABFOZms3lTQxqjmhy1FYV8dN1CPrpuIWfO9/DkziPxEfA7YjywvZ287CyuX1qVKCUMMK+8MN0hp8x0HGQiIiIiMppMXcG6EfgL4glgF/Ex7S+M9BytYM0ss/3Ld09vHy/sP0EkMZXw9WPnALi8roxwKN63dVlt2bTt27p4kAnEk+h777hiVn3OIiIiMn1M6xLB8VCCJTOVu7PnyNl431ZLlBcOnMAd5pUXxJOtxiDrllSSnzN9RsDPpjJQERERmRmme4mgiCSYGcsCJSwLlPA/bl7K0TPnebQtvrnxv7/wBv/y7H6K87K5uaGGpsYgtzYEqCjKS3fYIzo0RHI10nERERGR6UIJlsg0U12SzwfWLuADaxfQ1d3Lj/YcpbklxpbWKP/5ymGys4y1C+fQlBgBv6i6OKXXT0X5Zm1F4ZArWLUVM6fHTERERGYnlQiKzBB9fc4r7ScH+rbaDp8GYFmghHAoSFNjgFUL5pCdNf6+rVT1TqkHS0RERKYb9WCJzHIHj59jS2uUSGuMZ/ceo6fPqSrOY8PK+Aj49curKcob2yJ2KnunZvsgExEREZlelGCJyIBTXd08viM+Av7RHTFOd/WQn5PFjcuqCTcG2bgyQKCsYNTzzORNoEVERERGoiEXIjKgrCCX266q5baraunu7ePH+47TnCgl3NIWA+CqBRU0heKrWw3B0iFHwKeqd0qrVyIiIjJTaQVLZBZzd3ZGzwz0bb10sAOA+XMKE31bQa5dXEludhaQmt4p9V+JiIjIdKQSQREZs9ipLrYmRsA/ueso53v6KC3I4ZaGAE2NQW5eUcOjbbEJrT5pDywRERGZjlQiKCJjFigr4MPX1vPha+vpvNDLk7uOEGmNsqU1xkMvHyIny7huSSUfX7+YcCjIgsqiMV9De2CJiIjITDbuBMvMXnH3K1IZjIhkjsK8bN552Vzeedlcevuclw52EGmNEmmJ8gcPtfAHD7Wwcm4p4VCQcGOQK+vKyUpiBLz2wBIREZGZbMQSQTO7Y7i7gL9z95pJiWocVCIoMnVeP3o2nmy1Rvnx6yfo7XNqSvMJhwKEQ0FuWFZNQW72kM9VD5aIiIhMRynpwTKzbuDrMORk5ve7e+n4Q0wtJVgi6XHi7AUe2xkj0hLj8Z1HOHO+h8LcbNYvj4+A37AyQHVJ/tueoymCIiIiMt2kKsF6AfhFd391iPsOuvuCiYWZOkqwRNLvfE8vz+09PlBKeOhkF2Zwdf2cxFTCAEtrSoYcAS8iIiKSyVKVYK0H9rv7gSHuW+vuGZPRKMESySzuTsubp4i0xKcSvtJ+EoBFVUUDfVtrF84hJzECXkRERCSTTemYdjO7x93vnfCJJkAJlkhme/NkJ1ta48nWj3Yf40JvH+WFuWxYGR8Bf9OKGkryNdhUREREMtNUJ1gvuvvVEz7RBCjBEpk+zpzv4cmdR2hujfJoW4wT57rJy85i3dIqmkIBNoaCmiooIiIiGWWqE6zt7r56wieaACVYItNTT28fLx6Ij4Bvbomy7+hZAC6rLUv0bQW5rLZMfVsiIiKSVlrBEpFpac+RM0Ra4iPgt+0/gTvMKy9gY2IE/PVLq8jPGXoEvIiIiMhk0QqWiEx7x86cZ2tbvG/riZ1H6ezupTgvm5sbagiHgtzaEGBOcV66wxQREZFZINkEK1Ud5d9J0XlERAZUleTzgbUL+MDaBXR19/LMnmM0J0bA/+crh8kyWLuokqbEVMLF1cXpDllERERmuaRWsMysAPgV4DKgoP+4u39s8kIbG61gicwefX3Oq4dOEmmJ0twao/XNUwAsrSkm3BikKRRkdf0csrPUtyUiIiKpkdISQTP7DtAG/DzwBeBOoNXdPzHRQIe53lXA3wElwOvAne5+aqTnKMESmb3eOHFuYAT8M3uO0dPnVBbnDYyAX7+8mqI8jYAXERGR8Ut1grXd3Veb2U/c/UozywUecfcNqQh2iOv9GPiUuz9uZh8DFrv7Z0d6jhIsEQE41dXN4zuOEEmMgD/V1UNeThY3LqsmHAqyMRQgWFYw+olEREREBkl1D1Z34r8dZnY5cBhYNM7YktEAPJH4uRl4BBgxwRIRASgryOW2q2q57apaunv7+PHrx4m0xGhuPczWthh8F66aX0440be1cm6pRsCLiIhIyiS7gvVx4D+AK4B/Jl6691l3/8qkBGX2I+CP3f17ZvZJ4A/cvXSk52gFS0RG4u7sip2hOTECfvuBDgDqKgppagwSDgW5dnEleTlZaY5UREREMlGqSwQXu/u+0Y6NMcAIMHeIuz4N7AD+EqgCvg/8lrtXDXGOu4C7AOrr69fs379/vOGIyCwTO93Fo20xmltiPLX7CF3dfZQW5HBLQ4BwKMAtDQHKC3PTHaaIiIhkiFQnWJdsJJy4wJoJxJgUM1sB/Ku7XzvS47SCJSLj1Xmhl6d2HyXSEmVLW5SjZy6Qk2Vcu7iScChIU2OQBZVF6Q5TRERE0iglPVhmtpL4aPZyM7tj0F1lDBrXnmpmFnD3mJllAZ8hPlFQRGRSFOZl09QYT6T6+pyX3uggkigl/MIPWvjCD1poCJYSbgwQDgW5an4FWRoBLyIiIkMYcQXLzN4L3A68h3ipXr/TwDfd/UeTEpTZJ4BfT9x8ALjHR1lq0wqWiEyG/cfOEmmNEWmJ8vzrx+ntc6pL8gmH4snWjcurKcjNTneYIiIiMslSXSJ4vbs/k5LIJokSLBGZbCfPdfPYzhg/bIny+I4jnDnfQ0FuFuuX19AUCnLrygA1pfnpDlMm4MHt7dz/yA4OdXRSW1HI5k0N3L66Lt1hiYhIBkj1mPZjZrYFCLr75WZ2JfAed//DCUUpIjKNlBfl8t5Vdbx3VR0Xevp4bt+xRClhjOaWKGawekEF4cYgTaEgywIlGgE/jTy4vZ17HniFzu5eANo7OrnngVcAlGSJiEjSkl3BehzYDHzF3Vcnjr3q7pdPcnxJ0wqWiKSLu9P65mkirfG+rZ+8cRKAhVVF8f22QkGuWTSHnGyNgM9kN9y3lfaOzkuO11UU8vTdG9IQkYiIZJJUr2AVufvzF/0mtmdckYmIzDBmRmNtGY21ZfzWxuUcPtnFlrYokZYo//Lsfv7xqX2UF+ayYWW8b+umFdWUFmgEfKY5NERyNdLxfiorFBGRwZJNsI6a2VLAAczs/cCbkxaViMg0Nre8gDuvW8id1y3k7Pkentx1hOaWGFvbonx3ezu52ca6JVU0NQbZGApSV1GY7pAFqK0oHHIFq3aEz0dlhSIicrFkSwSXAH8PvAM4AewD7nT3jNnZVyWCIpLpevucFw+cINISpbk1yt4jZwFonFc20Ld1eV2Z+rbS5OJkCaAwN5t777hi2GRJZYUiIrNHSqcIDjppMZDl7qcnEtxkUIIlItPNniNn2NIapbklygv7T9DnMLesgI2hAOHGIO9YWkV+jkbAT6WxlvstvvthhvpX1IB9971r0uIUEZGpl9IeLDOrAj4H3Ai4mT0FfMHdj00sTBGR2WtpTQlLa0q466alHDtznkd3HCHSEi8j/PpzByjOy+amFTWEEyPgK4vz0h3yjHf76roxlfaNp6xQRERmtmR7sL4JPAG8L3H7TuBbQHgyghIRmW2qSvJ5/5r5vH/NfLq6e3lmb/8I+Cj/9ephsgzWLqwk3BgflLGkpiTdIQuweVPDkGWFmzc1pDEqERFJp2R7sF5w9zUXHduWzBLZVFGJoIjMRO7Oq+2naG6NTyVsefMUAEtqimkKBQk3Brm6fg7ZWenr25rtU/Qy/fVnenwiItNFSnuwzOxPgW3AtxOH3g9c5u6fm1CUKaQES0RmgzdOnGNrW3xj42f3HqO716kszuPWhgBNjUHWL6+mOD/Z4oSJG89gCJk6+nxERFInJQmWmZ0mPprdgGKg///Q2cDxuPWoAAAgAElEQVQZdy9LQawpoQRLRGab013dPL4z3re1tS3Gqa4e8nKyuGFpFeHG+AbHwbKCSY1BU/Qymz4fEZHUScmQC3cvTV1IIiKSSqUFubz7ylrefWUt3b19bHv9BJHEVMJHv/sqn/7uq1w5v5xwKJ5sheaVpnwE/Hg355Wpoc9HRGTqjVpHYmafcPe/MLPfdPcvT0VQIiIyNrnZWVy/tIrrl1bxmXeF2B07M9C39WeRnXypeSd1FYWEEyPgr1tcRV5O1oSvqyl6mU2fj4jI1EumUP+MmW0GNJJdRGQaMDOWB0tZHizl125ZRux0F4+2xWhuifGtbQf52jP7Kc3P4eaGGpoag9yyIkB5Ue64rqUpeplNn4+IyNQbrQfrc8R7r34L+AvgrLt/YYpiGxP1YImIjK7zQi9P7z5KpDVKpDXG0TPnyc4yrl1USbgxSFMoSH1V0ZjOqSl1mU2fj4hIaqRsiqCZ/SHwCLDJ3T+TovhSTgmWiMjY9PU5L7/REU+2WmLsiJ4GYEWwJN631Rhk1fwKstI4Al5ERCRTpDLBeqe7/9DMwu4eSVmEKaYES0RkYg4cO5dY2Yry3L7j9PY51SX5bFwZHwF/w7JqCvOy0x2miIhIWqR6HywDPgIsdvcvmFk9MNfdn594qKmhBEtEJHVOnuvmsZ3x/bYe33GE0+d7KMjN4sZlNTQ1BtiwMkhNaX66wxQREZkyqU6w/hboAza4e8jM5gA/dPdrJh5qaijBEhGZHBd6+nh+3/GBEfDtHZ2YwaoFFYRDQZoagywPlKR8BLyIiEgmSXWC9aK7X21m2919deLYy+5+VQpiTQklWCIik8/daTt8mkhLvJTw5TdOAlBfWZTo2wpwzaJKcrMnPgI+XTQUQkREhpKSjYYH6TazbMATJ68hvqIlIiKziJkRmldGaF4Zv7lxOYdPdrGlLb7f1r8+t59/enof5YW53NpQQ7gxyE0raigrGN8I+HR4cHv728aat3d0cs8DrwAoyRIRkaQku4J1J/Ah4Grga8D7gc+4+3fGfWGzDwCfB0LAte6+bdB99wC/AvQCv+Xuj4x2Pq1giYik19nzPTy5Kz4CfmtbjONnL5CbbaxbUkU4FGRjKMD8OWMbAT/Vbrhv65Ab89ZVFPL03RumNBatpImIZJaUlggmTrgS2AgYsMXdWycYYIj4KthXgE/1J1hm1gh8A7gWqAUiwAp37x3uXKAES0Qkk/T2OdsPnKC5Nb66tefIWQBC88poCgUINwa5vLY840bAL777YYb6V9GAffe9a8riuHglDeIbBN97xxVKskRE0iSlJYJmtg54zd3/OnG71Myuc/fnxhtgf4I2RFP0e4Fvuvt5YJ+Z7SaebD0z3muJiMjUys4y1i6qZO2iSu756RB7j5wZ2G/rrx7dzV9u3U2wLJ+NiSEZ1y+poiA3/SPgaysKh1zBqq0onNI47n9kx9uSK4DO7l7uf2SHEiwRkQyXbA/W3xIvD+x3dohjqVIHPDvo9huJYyIiMk0tqSnhrpoS7rppKcfPXuDRthiR1igPbm/n3547QFFeNjctj/dtbVgZoLI4Ly1xbt7UMOTK0eZNDVMax6EhkryLj8/UEsKZ+rpEZPZINsEyH1RL6O59Zjbqc80sAswd4q5Pu/v3hnvaEMeGrGM0s7uAuwDq6+tHC0dERDJAZXEe71szn/etmU9Xdy/P7j02sLr1368dJstgzcI5iamEQZbWlExZbP1f5NP9BX+0lbSZOoxjpr4uEZldkh1y8QDwGPFVK4BfA25199snHIDZY7y9B+seAHe/N3H7EeDz7j5iiaB6sEREpjd357VDp2hOjIB/7dApAJZUFxNuDBIOBVmzcA7ZGda3NRlG68HKpGEcqTRTX5eIzAypHtP+P4C/BD5DfDVpC4mVo0nwfeDfzOxLxIdcLAeen6RriYhIhjAzLq8r5/K6cn6naQXtHZ1sbY3yw5YoX316H3//xF7mFOWyYWWQpsYA65fXUJyf7D9j08toK2nJlBBORzP1dYnI7JLUv0zuHgM+nMoLm9nPAl8GaoCHzewld9/k7q+Z2beBFqAH+PXRJgiKiMjMU1dRyEevX8RHr1/E6a5untgZHwEfaY3yHy++QV52Fu9YFh8BHw4FmVtekO6QU+r21XXDlsVlyjCOVJupr0tEZpcRSwTN7Pfc/U/M7MsM0Qfl7r81mcGNhUoERURmh57ePrbtP0GkJUpza5T9x84BcEVdeaJvK0DjvLKhptTOGDN1jPtMfV0iMjOkZB8sM7vN3R8ys18c6n53/9oEYkwpJVgiIrOPu7PnyBmaW2I0txxm+8EO3KG2vGCgb2vdkirycrLSHWrKzdRpezP1dYnI9JeyjYbNLBu4z903pyq4yaAES0REjpw+z6NtMZpbozy56whd3X2U5Odwc0MNTaEgtzTUUFGUnhHwU0HJiYjI5ElZgpU42VZ3z+jxPUqwRERksK7uXp7e3d+3FePI6fNkZxnXLIqPgG9qDLKwqjjdYaaMyutERCZXqhOsLxKf5vcd4psMA+DuD0wkyFRSgiUiIsPp63N+0n6SSGIEfNvh0wAsD5QMlBKuXlBB1jQeAa8R5yIikyvVY9orgWPA4P9DO5AxCZaIiMhwsrKMVQsqWLWggk9tauDg8XMDEwn/4Ym9/O1je6guyWPjyvjmxjcuq6YwLzvdYY/JVI44VymiiMjwkk2wNrv70UmNREREZIosqCzil29YzC/fsJiT57p5bGeMSGuM/3zlTb617SD5OVmsX15NOBRkQyhAoHR8I+CnMhGZqhHnF5citnd0cs8DrwAoyRIRYZQEy8xuA/4J6DazPuCD7v6jKYlMRERkCpQX5fLeVXW8d1UdF3r6+PHrx2luidLcEu/dAli1oIKmRCnhimBJUiPgpzoR2bypYcgerM2bGlJ6nfsf2fG2awB0dvdy/yM7lGCJiDD6mPafEE+q2szsOuBP3P3mKYtuDNSDJSIiqeTu7Iieju+31RLl5TdOAlBfWTSw39Y1iyrJzR56BHw6eqKmYsVs8d0PX7oxJmDAvvveldJriYhkklT1YPW4exuAuz9nZqUpiU5ERCTDmRkr55axcm4Zv7FhOdFTXWxpjRFpjfKvz+3nn57eR1lBDreuDBAOBbm5oYaygtyB509lT1S/21fXTcoq0uDELcuM3iF+OZvqUkQRkelqtAQrYGafHO62u39pcsISERHJLMGyAn7+unp+/rp6zl3o4cldR4m0RNnaFuN7Lx0iJ8tYt6SKcCjAxlBwynqiJtvFpY5DJVeTUYooIjJdjZZg/QNQOsJtERGRWacoL4dNl81l02Vz6e1zXjp4guaW+OrW5x9q4fMPtVBbXkBOltHT91ZCMh0TkaF6rgCyzehz1xRBEZGLjJhgufsfTFUgIiIimWi0vqbsLGPNwkrWLKzk7p9eyb6jZ9nSGu/bevNU18DjivKy+ch1C/mpy+emJc7xGq6ksc9dPVciIkNIdky7iIjIrDOeSYCLq4v5+PolfHz9Ek6cvcCjO+IrW4/vOMLfP7mXf3l2PzetSIyAXxmgqiQ/LXEma6aUOoqITBUlWCIiIsOY6EjyOcV53HH1fO64ej7ne3p5du9xIi3xDY4feS2KGaypn0M4MQJ+WaAkpXH+7rdfBiaWZN26soZ/ffbAkMdHos2IRWS2UoIlIiIyjFROAszPyebmFTXcvKKGL7z3Ml47dIpIopTwvv9q477/amNJdfFAsnV1fQU5w4yATzaeXvcJr2Q92nZkyOPfeO4gaxdWDnlebUYsIrPZmBIsM2ty9+bJCkZERCSTTFZ5nJlxeV05l9eV89vhFRzq6Iz3bbXG+OrT+/j7J/YypyiXW1cGaAoFWb+ihpL84f/JHi5OGHrFbSyrS+NJ3rQZsYjMZmNdwfpjQAmWiIjMCps3NbxtJQZGnwQ4ntK42opCPnr9Ij56/SJOd3W/bQT8Ay+2k5edxfVLqxKrWwHmlb89wRsqzsEGJ0ljXV0aa/J28fWSOS4iMpOoRFBERGQY/YlDsglTKkrjSgty+Zkr5vEzV8yjp7ePF/afGCgl/OyDr/LZB+HyujLCoSBNjUEa55UNnPt3v/3ykPtUZZnx4PZ2bl9dl/TqUn+i2N7RiQGXnjVuqKRJgzFEZDYzH+J/xG97gNlXif9/1YDbgO/33+fuH5vU6MZg7dq1vm3btnSHISIis9gN920dMrGoqyjk6bs3TOjc7s6eI2dpTgzJePHACdyhtrxgoG8rdqqLz37vtSFXsgpzs7n3jiv4nW+9NGSyZDAwdv3iRHEkQ722oZ7ff31IPmEVEckkZvaCu68d7XHJrGD986CfbwS+Nt6gREREMtlEJ99NZmmcmbEsUMKyQAn/85alHD1znq1tMSItUb6z7Q3+7zP7KcnPoWFuKS8f7LgkiepfpUpmdWm4zYUvXskarlxyuJU/QMMvRGTGGzXBcvfH+382s9ODb4uIiMwUqSjvm8rSuOqSfD64dgEfXLuAru5efrTnKM0tMba0Rkcs5/uzD60ata9suITQia9YJZOA3r667pL7brhvq4ZfiMiMl9z817dcSNWFzewDZvaamfWZ2dpBx6vM7FEzO2Nmf5Wq64mIiIxkpN6kZG3e1EBhbvbbjo02FCMVCnKz2bAyyL13XMGz92ykZpjNi4vzc1hQWcQf3X45dRWFGPGE6d47rnhbgjNcQthfDrjvvnfx9N0bxpwUafiFiMwGYxpy4e7rUnjtV4E7gK9cdLwL+CxweeKPiIjIpEvFl/+xDsUYq2RKGLOyjE+/K3TJKpUBZ8738L6//RFZBtcsquRztzVy4/JqivLe/nVgPNMTk6HhFyIyG6RtiqC7t0K8pvyi42eBp8xsWTriEhGR2SlVX/6HKo0bTTKJ01hKGC9O9MoLczl7oYfu3njxYJ/Dc/uO89y+4+TnZHHjsmrCjUE2rgwQKCuYtERxshI3EZFMojHtIiIipO/L/0iJE7yV5GSZXTKCfaT+pcGJ3g33baWjs/uSx1QX53HbqlqaW6JsaYsBcNWCCppCAZoa5/LU/3vrJb8InYjJXuETEckEo45pn9DJzSLA3CHu+rS7fy/xmMeAT7n7toue+0vAWnf/jRHOfxdwF0B9ff2a/fv3pyhyERGZjSY6RXA8hhvtPqcol67uvlFHpQ8erz6cxXc/POJodndnZ/QMzS2HaW6N8fLBDgAWVBbG99sKBblmcSW52WNt3RYRmTlSOaZ93Nw9PMnn/3vg7yG+D9ZkXktERGa+8ZT3JWOkxG2o5ArgxLlLV5yGkkwJ42jlj2ZGw9xSGuaW8hsblhM71cWWxAj4f3vuAF99+nVKC3K4tSFAuDHIzStqKC/MTSo+EZHZZsQEy8xOM/Tm7Qa4u5dNSlQiIiIzxGi9U9lDlP4lK9kSxrGWPwbKCvi5a+v5uWvrOXehh6d2HSXSGmVLa4zvv3yInCzjuiWVhEPxDY4XVBaNK34RkZloUksER7yw2c8CXwZqgA7gJXfflLjvdaAMyEvc9053bxnpfGvXrvVt27aN9BAREZEpN1wJYP/I80V3Pzzuc//5h1YlveKWivLH3j7npYMdRFqjRFqi7IqdAWDl3FKaGuPJ1hV15WRlpa5vS0QkUyRbIjimBMvMAkBB/213PzC+8FJPCZaIiGSi0fqfxtuD1Z+gDTbVPWSvHz1LpDVKc0uUbftP0NvnBErz2RgK0tQY4B1Lqym4aF+wdMYrIjIRKe3BMrP3AF8EaoEYsBBoBS6bSJAiIiIz3Wj9T8OV733utvg/sX/w0GuX9GMNVd43ljHuqbKoupiPr1/Cx9cv4cTZCzy2M0akJcZDLx/iG88foDA3m/XL4yPgN6wMUD1oA+R0xDsVlDSKSFIrWGb2MrABiLj7ajO7Ffg5d79rsgNMllawREQkE12cSEA8Qbr3jisGvniP9qU8mS/to5UiTqXzPb08t/f4QCnhoZNdmMHV9XPiUwkbA/zCPz7PoZNdGRFvqiTzWYvI9JXSEkEz2+buaxOJ1mp37zOz59392lQEmwpKsEREJFNNxarGaKWI6eLutLx5ikhLjObWw7zafmrEx6c73onIpCRXRFIv1WPaO8ysBHgC+LqZxYCeiQQoIiIyW0zW+PfBRitFTBcz47Laci6rLecT4eW8ebKTSGuMP/xBC+d7+i55/NzygiHOMj0cGmbk/nDHRWRmSnbHwPcCncDvAP8N7AFum6ygREREZGw2b2qg8KKBEsmOcZ9K88oL+ei6hfzx+66kIOfSryGx0+f5hX96nn955vVpl5gMl8ymO8kVkamVtjHtqaYSQRERme2m24CFwfHOKy/gfWvmc76nj+aWKPuOngXgstqygRHwl9WWYZa5I+DVgyUys6W6B2vwhsN5QC5wNpM2GlaCJSIiMnPsjp0ZGJLxwoETuMO88oL45saNQdYtqSQ/Z/gR8Oky3ZJcEUnepOyDNejktwPXuvv/Gk9wk0EJloiIyMx07Mx5trbFiLRGeWLnUTq7eynOy+bmhhrCoSC3NgSYU5yX7jBFZIab1AQrcYFn3X3duJ48CZRgiYiITJ10rdR0dffyzJ5jNCdWt2Knz5NlsHZRJU2J1a3F1cWTHoeIzD6pLhG8Y9DNLGAtcLO7Xz/+EFNLCZaIiMjUyJReo74+59VDJ4m0RPlhS5S2w6cBWBYoGdhva9WCOWRnDd+3pZI+EUlWqhOsrw662QO8DvyDu8fGHWGKKcESERGZGpm639PB4+fY0hol0hrj2b3H6Olzqorz2LAyQLgxyPrl1RTlvbVDTaYkiiIyPaR0Hyx3/+WJhyQiIiIzQabu97SgsohfumExv3TDYk51dfP4jiNEWqM88tphvvPCG+TlZHHjsmrCoSAbQwHuf2TH25IrgM7uXu5/ZIcSLBEZtxETLDP7Mgy5MTwA7v5bKY9IREREMlqmbmo8WFlBLrddVcttV9XS3dvHj18/TqQlRnPrYba2xeC7wz833YmiiExvo200vA14ASgArgZ2Jf6sAnpHeJ6IiIjMUNNlU+N+udlZvGNpNb9/WyNPbL6VH/7OTWze1EBu9tC9WfPKC6Y4QhGZSUZcwXL3rwGY2S8Bt7p7d+L23wE/nPToREREJOP0l89Nx+EQZsaKYCkrgqXUVRRy93/8hK6evrc95vi5C/zmN7YTDgW4pSFAeWFumqIVkekoqR4soBYoBY4nbpckjomIiMgkyeQJd7evrsuYWMbr4kRxbnkB77piHqe7etjSFuWhlw+Rk2Vcu7gyMZUwyILKojRHLSKZLtkpgr8MfB54NHHoZuDz/StcmUBTBEVEZCbRhLv06utzXnqjg0hLlOaWKLtiZwBYObeUcGK/rSvryskaYQS8iMwsKd9o2MzmAtclbj7n7ocnEF/KKcESEZGZJFNHoc9Wrx89S6Q1SqQ1yo9fP0Fvn1NTmk84FCAcCnLDsmoKLupLE5GZJSVj2s1spbu3mdnViUMHE/+tNbNad39xooGKiIjIpTJ1FPp4ZXK5YzIWVRfz8fVL+Pj6JXScu8BjO47Q3BrloZff5BvPH6QgN4v1y2toCgW5dWWAmtL8dIcsImkyWg/WJ4G7gC8OcZ8D+hWaiIjIJJgOo9CTdXG5Y3tHJ/c88ArAtEqy+lUU5Q30oF3o6eO5fceItMQ3OG5uiWIGqxdUEG4M8s7GIEtrSjBTKaHIbJF0iWCmU4mgiIjMJDOpB2u2lDu6O61vnqa5JV5K+Er7SQAWVRUN9G2tXTiHnOzRdskRkUyUkhLBQSf7APDf7n7azD5DfE+s/+3u2ycQ4AeID84IAde6+7bE8SbgPiAPuABsdvet472OiIjIdDSdR6FfbKaVOw7HzGisLaOxtoxPhJfz5slOtrTGiLRG+b/P7Of/PLWP8sJcNqyM923dtKKa0gKNgBeZaZId0/5Zd/+Omd0IbAL+FPg73hp6MR6vAncAX7no+FHgNnc/ZGaXA48A0+9fExERkQmaCaPQYWaVO47FvPJCPrJuIR9Zt5Cz53t4ctcRmltibG2L8t3t7eRmG+uWVNHUGGRjKEjdDH8/RGaLZMe0b3f31WZ2L/CKu/9b/7EJB2D2GPCp/hWsi+4z4glXrbufH+k8KhEUERHJTDOp3DEVevucFw+cGBgBv/foWQAa55XR1Bjfb+uy2jL1bYlkmJSWCALtZvYVIAz8sZnlA1NRQPw+YPtoyZWIiIhkrplU7pgK2VnGNYsquWZRJff8TIg9R84khmRE+fLWXfzFll3MLSsg3BgvJbx+aRX5ORoBLzJdJLuCVQT8FPHVq11mNg+4wt1/OMrzIsDcIe76tLt/L/GYxxhiBcvMLgO+D7zT3fcMc/67iE85pL6+fs3+/ftHfS0iIiIimerYmfM8uuMIkZYoT+w6wrkLvRTnZXPTihrCiRHwlcV56Q5TZFaajI2GbwSWu/tXzawGKHH3fROMc8gEy8zmA1uBX3b3p5M5j0oERUREZCbp6u7lmb3HBla3oqfOk2WwdmEl4cYATY1zWVxdnO4wRWaNlCZYZvY5YC3Q4O4rzKwW+I6735CCQB9jUIJlZhXA48AX3P0/kj2PEiwREZHMNN03Gc4E7s6r7adobjlMc2uM1jdPAbC0pphwY5CmUJDV9XPIzlLflshkSXWC9RKwGnixf7CFmf3E3a+cQIA/C3wZqAE6gJfcfVNiDPw9wK5BD3+nu8dGOp8SLBERkcyjAReT440T5wZGwD+79xjdvU5lcd7ACPj1y6spzk+21V5EkpHqBOt5d7/WzF5096vNrBh4ZiIJVqopwRIREck8s2WT4XQ61dXNEzvjfVtb22Kc6uohLyeLG5ZWEW4MEg4FCZYVpDtMkWkv1VMEv52YIlhhZr8KfAz4PxMJUERERGa+2bLJcDqVFeTy7itrefeVtXT39rHt9RNEWuMj4B/97qt8+ruvcuX8cppCQcKNQVbOLdUI+DRT2ezMNpYhF03AOwEDHnH35skMbKy0giUiIpJ5tIKVPu7OrtgZmhNDMl462IF7/L1vSqxsXbu4krycqdh5R/qpbHb6SvkUwYtOng182N2/Pp7gJoMSLBERkcyjL5OZI3a6i0fbYjS3xHhq9xG6uvsozc/h5oYamhqD3LIiQHlRbrrDnPH0S4fpKyUlgmZWBvw6UEd8T6rmxO3NwEtAxiRYIiIiknm0yXDmCJQW8KFr6vnQNfV0Xujl6d1HibRGibTG+MFP3iQ7y7h2UeXAVML6qqJ0hzwp0l2eN1lls+l+XfKWEVewzOx7wAngGWAjMAfIAz7h7i9NSYRJ0gqWiIiIyNj19Tkvv9ExUEq4M3oGgIZgKeHG+FTCq+ZXkDUDRsBnworqcCtY2WZ88YNXjSuOTHhds0FKSgTN7BV3vyLxczZwFKh399MpizRFlGCJiIiITNz+Y2eJtMaItER5/vXj9PY51SX5hEPxZOuGZdUU5mW/7TnTZfUkE8rzhkqG+o03KcqE1zUbpGqKYHf/D+7ea2b7MjG5EhEREZHUWFhVzK/cuJhfuXExJ89189jOGM0tUR7+yZt888cHKcjN4sZlNTQ1BtiwMsjTu4++LWFo7+jkngdeAci4JCsTplr2vye/++2X6b1ooaOzu5f7H9kx5vctE16XvGW0BOsqMzuV+NmAwsRtA9zdyyY1OhERERFJm/KiXN67qo73rqrjQk8fz+87PjACPtIaxewVcrKM7t7UJAqTrbaicMiVntqKwimP5eLkqt94kqJMel0CI87ldPdsdy9L/Cl195xBPyu5EhEREZkl8nKyuHF5NasWVNDfYlKSn3NJctVvqC/86bZ5UwO5F/WS5WYZmzc1TFkM/SWCwxlPUrR5UwOFuW8v2yzMzZ7S1yVv0cYHIiIiIpKU/uTg0MkuAE539TDc6Asz+O1vbucHPznE6a7uYR6VBhcHPMWzO+5/ZMeQ/Vcw/qTo9tV13HvHFdRVFGLEe6804CJ9RisRFBEREREBhk4OnETvyKBjedlZrFpQwRO7jvLgS4fIzTbWLakiHAqyMRRg/pz0jIC//5Edl6y4dff6lJYzjlQCOJGk6PbVdUqoMoQSLBERERFJynDJgRNfNbl4imBvn7P9wAmaE31bn/v+a3zu+6/ROK9sYL+ty+vKMJuaZaTxDoNI5ZTE4fql6ioKlSDNECOOaZ9ONKZdREREZHJNdBz4niNn2NIaJdISY9v+4/Q5zC0rYGMoQLgxyPVLqii4qJcolZKNf3BCVVGUy5muHrr73vrOnJttFOflcLKze8wJl/asmr5SNaZdRERERASID1MYKjlItm9oaU0JS2tKuOumpRw/e4FH22JEWqN8d3s7X3/uAEV52dy0vIZwY5ANKwNUFudNefwXJ0Anzl3aP9bd63R0xo+PdSx9/2Omw75hMj5awRIRERGRpE3GpsJd3b08u/cYkcTq1uFTXWQZrFk4h3AoSFNjkCU1JZMe/4Pb24fcnyoZ2tR35kt2BUsJloiIiIhkDHfn1fZTNLdGibREaXkzviXrkppimkJBwo1Brq6fQ3ZWavu2hirdGwsD9t33rpTGJJlFJYIiIiIiaTIZqzyZYrJfm5lxxfxyrphfziebVtDe0cmWxJCMf3p6H195Yi+VxXnc2hCgqTHA+uU1FOdP/CvtSOPTk9G/f9VM/uwlOVrBEhEREUmhmTzEIN2v7XRXN0/sPEqkNcrWthgnO7vJy87iHcviI+DDoSBzywuSPt/gZGikb8SDh1oMNfSi/z0AZuxnLyoRFBEREUmLiU7ay2SZ9Np6evvYtv8EkZYoza1R9h87B8CV88sHkq3QvNJhR8CPtSSwbtBq1HCrVJn0/kjqqURQREREJA3Gu9fSdJBJry0nO4t1S/7/9u49OuryzuP4+zu5hwAJSWaAEBDkNkEPChSpFBVIdK3Vqq0ee/a0trtdt7rtVnd1i+tZpee4R1p66vZydnvTPetOPzwAABZ6SURBVLan26qtVXfVYgJq0XrXUCQBBAUVdEJE7gFyefaP+SXGOJPL5De35PM6Z05++f1+M3nm65iHb57n+T7lLJlRzi0XhdnRcqRn3dadDdv5fv12qkqLqPVKwJ81vZz83EDP84c6JbBvtcBYI1KZFB9JHyVYIiIiIj6Kt5Fs9xqdbJap783MmBUay6zQWK47byb7Dp/gia0t1DdHuPelt7nn2d2MLcjlnDmV1IVDnDenst+kx4CA2ceqCba1d7J23ba40/0yNT6SWoGBb0kOM7vCzLaYWZeZLep1frGZNXqPTWZ2WbraKCIiIjJUN10wh6I+m+UOZa+oTJYt761ybAFXfqKan39pEY23ns9dVy/iM/Mn8cKb+7n+3kYW3t5AXm78fwZPLi2KW6q9v8QsVnwAjp3s4MFX9wz9jUhWStsaLDMLA13AT4EbnXMveeeLgZPOuQ4zmwRsAiY75zr6ez2twRIREZFMMZIryWXze+vqcvxlz0Hqm97jgVf28O7B43HvNYhZ+KJ7LVZ/e2mtfnhLz0bE3TKx2EWi/y2z+TMwHFlT5MLMnqRXgtXn2nTgOaBKCZaIiIiI+OkXG9/gRxt2cLBPMhRPUV4On1tYxe9f3tNvpcBsKHaRaEXIdFeSTKfBJlhpmyLYHzM7y8y2AJuBrw2UXImIiIiMRg++uoelazYwfdUjLF2zQdPQhuiry2aw6bbz6W/L4kJvKuHEcYXccfnpPLF138eKY3SvzeqWDcUuYhX56Ps+/HzeaJLUIhdm1gBMjHHpFufcQ/Ge55x7HpjnTSO8x8wec859bAzXzK4BrgGYOnWqT60WERERyXx9RxL6VrmTwYtXnGJMfg6lxfnsOdBG5PBx7nl2V8z74KPJk1/FLpI5FS/RJDAbksd0S+oIlnOu1jl3WoxH3OSqz/ObgaPAaXGu/8w5t8g5t6iystLPpouIiIhkNI0k+Cde8Y5/v+x0nv7Wch775jL+qXY2XV3xl9b0Tp78KAbSnUDv8TZB7k6g/RqljJfsDZQEJvq80STjpgia2XQzy/WOpwFzgF1pbZSIiIhIhtFIgn8uPbOKOy4/narSIozoWqnuNUVmRnjSOL6xchYPff1TfPuSeeTlfHxS4cRxhfzvpr0cOt7e7+sNpHva5/X3NiY1gU40CcyWSpLplLZ9sLzy6z8CKoFHzKzROXcB8ClglZm1E60yeJ1zrjVd7RQRERHJRNpzyV/xNg/u6+qzT2F8UR5r121jz4E2JhTnMzNUws6WI3zjN6+SGzCWzCinNhzk3r9fwpSy4kG3IVYBib78SqC73+tQpyAm+rzRJO1VBP2iKoIiIiIymozmam6ZqLPL0fj2BzzeFKGhKcLOfUcBCE8aR104SG1NiNMmjycQiF9SI171wd4yqRLhaDPYKoJpG8ESERERkcRpJCGz5ASMhdMmsHDaBG6+MMwb+46wvrmF+uYIP35iBz/csIPQuAJWhkPUhUN88tRyCvtMtRtodEpT8bKDRrBERERERJLog6MneWJbCw3NEZ7ato+jJzspzs9h2awKasMhVswNUl5S0O8IVpUS6LTTCJaIiIiISAYoG5PP5QumcPmCKZzo6OS5N/bT0BShoTnCui0RzGDh1DIWTiuj9cgJTnR09TxX0z6zj0awRERERCTjJWNPqGTuMzUYzjm27D1EvZdsbdl7CIDcgNHR5agoyefmC8N8buGUlLVJ4hvsCJYSLBERERHJaMko6DHQa6Yj+dp7oI31zRHqm1t4dmcr7Z2OsuI8ls8NUhcOsWx2JSUFmoCWLkqwRERERGREiLc2aTgV9fp7zZsumJP2Co2Hj7ez8fVWGpoibNjWwoFj7eTnBDh7Zjm14RArw0EmjVdJ/lRSgiUiIiIiI8L0VY8Q61+sBry55iLfXzPeHmPpKpHe0dnFy7s/oL4pQn1zhN3vHwPg9Krx1IZD1NYEqZk0DrP4JeBl+FTkQkRERESyVu8pegEzOmMMCgxnU+X+NmqOVy7dr01+hyo3J8BZM8o5a0Y5t1wUZue+I9Q3RasS/sf67dzZsJ3J4wuprQlRGw6xZEY5+bmBtLRVlGCJiIiISIbpuz4qVnI13D2h4k0DvOmCOaxdty1u8pVuZsbM4FhmBsdy7Xmn0nrkBBu2ttDQFOH+l97hl8/upqQgl3NnV1JbE2T5nCClxfnpbvaoogRLRERERDLK2nXbPpL4dMsxo8s5X4pOxNuoGeDoiY6P3Z+pm/xWlBRw5aJqrlxUzfH2Tv68s7VndOuRze+SEzAWTSujriZEXU2IaeVj0t3kEU9rsEREREQkoyRjzdVgxKosCFBWnMdtF8/Lmr2oHnx1D9/941b2HjzO2IJcSgpzeffgcQBmBUt6phKeUV1KTkDrtgZLa7BEREREJCv1tz4qmeKNnBXn5w6YXKV7T63e7eidJB4+0UFHl+PfLgoTCBgNzRF+/qc3+K8nd1JRks+KuUFqwyGWzaqkKD8n5e0diZRgiYiIiEhG6W99VDIlWtyib1Kz50AbNz+wGSDlSVasJLGtvZO7n9nFM6tW8JWl0znY1s5T2/fR0BThsdfe476X3qEgN8CyWRXUhkOsCAcJji1MabtHEiVYIiIiIpJR4q2PSnaykujIWbykZu26bSkf+RpMkji+KI9L5k/mkvmTae/s4sU391PfHKG+KUJDcwsAZ1SXUudNJZwdKlEJ+CFQgiUiIiIiGefSM6tSPvqT6MjZQElNvCQqGSNfQ00S83ICnD2zgrNnVnDrZ2rYFjlMQ1OE+uYW1q7bxtp126ieUERtOERdOMQnpk8gL0cl4PujBEtERERERr3uJKitvZMcb9+tqkGOKPWX1PSXRK1+eEvCI1/xDGd6pZkxd+I45k4cx9dXzKLl0HHWeyXg/+f5t/jvZ3YxtjCX5XOC1NaEOHd2JeOL8hJq50imBEtERERERrV4+24dO/nxcu2xDLSnVqwkavXDWzjQ1h7z9fYeaEt46qCf0yuD4wr5wuKpfGHxVI6d7ODp11tpaI6wvrmFhzftJTdgnDVjAnXhECvDIaonFA/5Z4xEKtMuIiIiIqPa0jUbYo5AAeTlGGs/Pz/htVTxSs73p6w4j+PtXR9L2O64/PSMKBXf2eVofPuDnv22drQcAWDuxLE967ZOrxpPIMES8JlSkbGvwZZpV4IlIiIiIqPaQElQWXEer956fkKv3V/yFk9pUV7M0a2q0iKeWbUioXYk05utR1nvFcl4cdd+uhwExxawMhyiribI2adWUJg3uBLwsfYiy5TkUvtgiYiIiIgMQrw1VN0+OBZ7Kt9gxJs+WJgXiPm6ZcV5HIjz8wYqF58u0yvG8NVlM/jqshl8cPQkT25voaGphYcb9/CbF96iKC8nWgK+JsTKuUHKSwrivtZwKjJmCiVYIiIiIjKqxUqC/BJvTRQQM/G67eJ5rF23LS0bLfuhbEw+l505hcvOnMKJjk6ef2M/Dc0RGpoiPN4UwQwWTi2j1ptKeGrlmI+UgE90L7JMkrYEy8yuAFYDYWCxc+6lPtenAk3Aaufc91LfQhEREREZDbqToBvubYw5VbB0mJXy+is5H2+tUTo2WvZbQW4O58yu5JzZlXz7knls2Xsommw1R1jz2FbWPLaV6RVjqA0HqQ2HWDitLOG9yDJJOkewXgMuB34a5/qdwGOpa46IiIiIjFbdic1N92+ivevDNCsvYKy+ZF7SfmasxCtdGy0nk5lxWtV4Tqsaz/W1s9l7oK2nBPw9f97Nzze+SWlxHjMrS9h3+AQnO7t6npttyWXaEiznXDMQc1doM7sUeAM4muJmiYiIiMgolUmJTTo2Wk6lyaVFfHHJNL64ZBpHTnSwcfs+6psjbNja8pHkanxRHjfUzsqqWKS9iqCZPQnc2D1F0MzGAA1AHXAjcGQwUwRVRVBERERExH+pLJve0dnFK28doL7pPeqbIux6/xgAp1WNozYcXbc1b/K4mIM0yZYRZdrNrAGYGOPSLc65h7x7nuSjCdb3gBecc/eZ2Wr6SbDM7BrgGoCpU6cu3L17t/9vQkREREQkwyUrCUpn2XTnHDv3He0pkvHyWx/gHEwaXxhNtmpCLJkxgYLcwZWAH66MSLAGI0aCtRGo9i6XAl3Arc65H/f3OhrBEhEREZGRYigJUzKToHj7eKVjT673j5xgw9bo5sZ/2t5KW3snY/JzOHdOJbXhEMvnBCkbk5+0n5+1+2A555Z1H/caweo3uRIRERERGSn6Jkx7DrRx8wObAWImTInuHTWYJC6TyqaXlxRwxaJqrlhUzfH2Tp7d+T713ujWo5vfIydgLJpWRp1XAv6UijEpbyOkt0z7ZcCPgErgETNrdM5dkK72iIiIiIhkgqEmTIkkQYNN4jK1bHphXg7L5wZZPjfI7Z89jc17DtLQHKG+KcLtjzRz+yPNzAyWUBsOUVcT5IzqMnICqVm3lc4qgn8A/jDAPatT0xoRERERkcww1IQpkSRosElcrE2YM61seiBgzK8uZX51Kf98/hze3n+M9c0RGppb+MXGN/jJUzspH5PPirlBamtCLJtVQXF+8tKgjJsiKCIiIiIymg01YUokCRpsEpdJpesHq3pCMV9eOp0vL53OoePtPLVtHw3NEdZteY/7X36HgtwAn5pZQW1NiJVzgwTHFfr685VgiYiIiIhkkKEmTIkkQUNJ4rJ5T65xhXlcPH8yF8+fTHtnFy/u2k99U4SG5gjrt7YAML+6lLpwdHRrTmjssEvAp72KoF9URVBERERERopk7z2VzvLrmcA5x/bIkZ51W41vHwBgSlmRt24rxOLpE8jLCfQ8J2vKtPtFCZaIiIiIyOClcgPhTNdy6HhPCfiNr7dyoqOLsYW5nDcnSF1NiHNnV1JanK8ES0REREREZCjaTnby9I5WGpoirN8aofXISXIDxs47LsrOfbBERERERETSpSg/h7qa6DTBri5H4zsHqG+KsOqOwT1fCZaIiIiIiEgMgYCxYGoZC6aWsWqwz0lqi0REREREREYRJVgiIiIiIiI+UYIlIiIiIiLiEyVYIiIiIiIiPhkxZdrNbB+wOwU/qgJoTcHPkSjFO3UU69RSvFNHsU4txTt1FOvUUrxTJ1NjPc05VznQTSMmwUoVM3tpMPXvxR+Kd+oo1qmleKeOYp1ainfqKNappXinTrbHWlMERUREREREfKIES0RERERExCdKsIbuZ+luwCijeKeOYp1ainfqKNappXinjmKdWop36mR1rLUGS0RERERExCcawRIREREREfGJEqw+zOxuM2sxs9diXLvRzJyZVXjfm5n90Mx2mNlfzGxB6luc3eLF28y+YWbbzGyLmX231/mbvXhvM7MLUt/i7BUr1mZ2hpk9Z2aNZvaSmS32zuuzPQxmVm1mT5hZs/cZ/qZ3foKZ1ZvZ697XMu+84j0M/cR7rZlt9WL6BzMr7fUc/S5JQLxY97quftIn/cVafaT/+vk9on7SZ2ZWaGYvmNkmL9bf9s5PN7PnvT7yXjPL984XeN/v8K6fks72D4pzTo9eD+AcYAHwWp/z1cA6onttVXjnPg08BhiwBHg+3e3PtkeseAPLgQagwPs+6H2tATYBBcB0YCeQk+73kC2POLF+HLjQO/408GSvY322E4/1JGCBdzwW2O59fr8LrPLOrwK+o3gnNd7nA7ne+e/0ird+l/gca+979ZMpiLX6yJTHW/2k/7E2oMQ7zgOe92J4H3CVd/4nwLXe8XXAT7zjq4B70/0eBnpoBKsP59yfgP0xLt0J/AvQe9HaZ4FfuqjngFIzm5SCZo4YceJ9LbDGOXfCu6fFO/9Z4LfOuRPOuTeBHcDilDU2y8WJtQPGecfjgb3esT7bw+Cce9c594p3fBhoBqqIxvUe77Z7gEu9Y8V7GOLF2zn3uHOuw7vtOWCKd6zfJQnq57MN6id91U+s1UcmQT/xVj/pMy9mR7xv87yHA1YAv/PO9+0ju/vO3wErzcxS1NyEKMEaBDO7BNjjnNvU51IV8Hav79/hw45GEjcbWOYNAz9lZp/wzive/rseWGtmbwPfA272zivWPvGmMpxJ9C90IefcuxDtzIGgd5vi7ZM+8e7tb4j+tRkUb1/0jrX6yeTq87lWH5lkfeKtfjIJzCzHzBqBFqCe6IjrgV5/FOsdz55Ye9cPAuWpbfHQKMEagJkVA7cAt8a6HOOcyjIOXy5QRnS4+CbgPu8vFYq3/64FbnDOVQM3AHd55xVrH5hZCfB74Hrn3KH+bo1xTvEeonjxNrNbgA7g192nYjxd8R6C3rEmGlv1k0kS43OtPjKJYsRb/WQSOOc6nXNnEJ1ZsBgIx7rN+5p1sVaCNbBTic5l3mRmu4h+EF4xs4lEs+vqXvdO4cOhY0ncO8AD3hDyC0AXUIHinQxXAw94x/fz4XQSxXqYzCyPaCf9a+dcd4wj3VNIvK/dU3sU72GKE2/M7GrgM8BfO28CP4r3sMSItfrJJInzuVYfmSRx4q1+MomccweAJ4n+waDUzHK9S73j2RNr7/p4Yi/nyRhKsAbgnNvsnAs6505xzp1C9D/yAufce8DDwJe8SjJLgIPd039kWB4kOg8XM5sN5AOtRON9lVdNZjowC3ghba0cGfYC53rHK4DXvWN9tofB+2vyXUCzc+77vS49TLSzxvv6UK/zineC4sXbzP4K+BZwiXPuWK+n6HdJgmLFWv1kcvTze0R9ZBL0E2/1kz4zs0rzqrqaWRFQS3TN2xPA573b+vaR3X3n54ENvf5glpFyB75ldDGz3wDnARVm9g5wm3Purji3P0q0iswO4BjwlZQ0cgSJFW/gbuBui5YTPwlc7f2PtMXM7gOaiE5J+QfnXGd6Wp594sT674AfeH8ROg5c492uz/bwLAW+CGz25pgD/Cuwhuh0nr8F3gKu8K4p3sMTL94/JFpRrd5bD/2cc+5rzjn9LklczFg75x6Nc78+24mL97lWH5kc8eKtftJ/k4B7zCyH6GDPfc65/zOzJuC3ZnY78CofTse8C/iVme0gOnJ1VToaPRSW4QmgiIiIiIhI1tAUQREREREREZ8owRIREREREfGJEiwRERERERGfKMESERERERHxiRIsERERERERnyjBEhGREcPMOs2s0cxeM7P7zaw4zn2Pdu/DIiIi4ieVaRcRkRHDzI4450q8418DL/fZfNiI9n1d6WqjiIiMbBrBEhGRkWojMNPMTjGzZjP7T+AVoNrMdplZBYCZfcnM/mJmm8zsV965SjP7vZm96D2WpvF9iIhIFslNdwNERET8Zma5wIXAH71Tc4CvOOeu86533zcPuAVY6pxrNbMJ3v0/AO50zj1tZlOBdUA4hW9BRESylBIsEREZSYrMrNE73gjcBUwGdjvnnotx/wrgd865VgDn3H7vfC1Q052IAePMbKxz7nDymi4iIiOBEiwRERlJ2pxzZ/Q+4SVJR+Pcb0CsxcgB4JPOuTZ/myciIiOd1mCJiMhoth640szKAXpNEXwc+Hr3TWZ2RoznioiIfIwSLBERGbWcc1uAfweeMrNNQHfFwX8EFnnFL5qAr6WrjSIikl1Upl1ERERERMQnGsESERERERHxiRIsERERERERnyjBEhERERER8YkSLBEREREREZ8owRIREREREfGJEiwRERERERGfKMESERERERHxiRIsERERERERn/w/lu+1YDpUMxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f75d4604a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "fig = sm.graphics.plot_ccpr_grid(beef_model, fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression plots\n",
    "\n",
    "We use plot_regress_exog function to quickly check model assumptions with respect to a single regressor, Price in our case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f75d4c6358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_regress_exog(beef_model, 'Price', fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Least Square (RLS)\n",
    "\n",
    "Finally we apply Recursive Least Square (RLS) filter to investigate parameter instability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "beef['Year'] = pd.to_datetime(beef['Year'], format=\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>22.9976</td>\n",
       "      <td>142.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>22.6131</td>\n",
       "      <td>143.9333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>23.4054</td>\n",
       "      <td>146.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>22.7401</td>\n",
       "      <td>150.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1978-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0441</td>\n",
       "      <td>160.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year  Quarter  Quantity     Price\n",
       "0 1977-01-01        1   22.9976  142.1667\n",
       "1 1977-01-01        2   22.6131  143.9333\n",
       "2 1977-01-01        3   23.4054  146.5000\n",
       "3 1977-01-01        4   22.7401  150.8000\n",
       "4 1978-01-01        1   22.0441  160.0000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import *\n",
    "beef['Date'] = beef.apply(lambda x:(x['Year'] + BQuarterBegin(x['Quarter'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "beef.drop(['Year', 'Quarter'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "beef.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1977-03-01</th>\n",
       "      <td>22.9976</td>\n",
       "      <td>142.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-06-01</th>\n",
       "      <td>22.6131</td>\n",
       "      <td>143.9333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-01</th>\n",
       "      <td>23.4054</td>\n",
       "      <td>146.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-12-01</th>\n",
       "      <td>22.7401</td>\n",
       "      <td>150.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03-01</th>\n",
       "      <td>22.0441</td>\n",
       "      <td>160.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-01</th>\n",
       "      <td>21.7602</td>\n",
       "      <td>182.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-01</th>\n",
       "      <td>21.6064</td>\n",
       "      <td>186.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-01</th>\n",
       "      <td>21.8814</td>\n",
       "      <td>186.4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-03-01</th>\n",
       "      <td>20.5086</td>\n",
       "      <td>211.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-06-01</th>\n",
       "      <td>19.0408</td>\n",
       "      <td>231.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity     Price\n",
       "Date                          \n",
       "1977-03-01   22.9976  142.1667\n",
       "1977-06-01   22.6131  143.9333\n",
       "1977-09-01   23.4054  146.5000\n",
       "1977-12-01   22.7401  150.8000\n",
       "1978-03-01   22.0441  160.0000\n",
       "1978-06-01   21.7602  182.5333\n",
       "1978-09-01   21.6064  186.2000\n",
       "1978-12-01   21.8814  186.4333\n",
       "1979-03-01   20.5086  211.7000\n",
       "1979-06-01   19.0408  231.5000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beef.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "endog = beef['Quantity']\n",
    "\n",
    "# To the regressors in the dataset, we add a column of ones for an intercept\n",
    "exog = sm.add_constant(beef['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RLS estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Statespace Model Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               Quantity   No. Observations:                   91\n",
      "Model:                    RecursiveLS   Log Likelihood                 -84.326\n",
      "Date:                Fri, 31 Aug 2018   AIC                            172.653\n",
      "Time:                        23:38:41   BIC                            177.675\n",
      "Sample:                    03-01-1977   HQIC                           174.679\n",
      "                         - 09-01-1999                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         30.0515      0.413     72.700      0.000      29.241      30.862\n",
      "Price         -0.0465      0.002    -28.482      0.000      -0.050      -0.043\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                      422.30   Jarque-Bera (JB):                 1.37\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.50\n",
      "Heteroskedasticity (H):               1.35   Skew:                             0.10\n",
      "Prob(H) (two-sided):                  0.41   Kurtosis:                         2.43\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Parameters and covariance matrix estimates are RLS estimates conditional on the entire sample.\n"
     ]
    }
   ],
   "source": [
    "mod = sm.RecursiveLS(endog, exog)\n",
    "res = mod.fit()\n",
    "\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RLS model computes the regression parameters recursively, so there are as many estimates as there are datapoints, the summary table only presents the regression parameters estimated on the entire sample; these estimates are equivalent to OLS estimates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RLS plots\n",
    "\n",
    "We can generate the recursively estimated coefficients plot on a given variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f75de47978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot_recursive_coefficient(range(mod.k_exog), alpha=None, figsize=(10,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we visually check for parameter stability using the plot_cusum function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f75e7faa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res.plot_cusum(figsize=(10,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, the CUSUM statistic does not move outside of the 5% significance bands, so we fail to reject the null hypothesis of stable parameters at the 5% level."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
